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What and Who

Data Privacy in the Decentralized Era

Amrita Roy Chowdhury
University of California-San Diego
CIS@MPG Colloquium

Amrita Roy Chowdhury is a CRA/CCC CIFellow at University of California-San Diego, working with Prof. Kamalika Chaudhuri. She graduated with her PhD from University of Wisconsin-Madison and was advised by Prof. Somesh Jha. She completed her Bachelor of Engineering in Computer Science from the Indian Institute of Engineering Science and Technology, Shibpur where she was awarded the President of India Gold Medal. Her work explores the synergy between differential privacy and cryptography through novel algorithms that expose the rich interconnections between the two areas, both in theory and practice. She has been recognized as a Rising Star in EECS in 2020 and 2021, and a Facebook Fellowship finalist, 2021. She has also been selected as a UChicago Rising Star in Data Science, 2021.
AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI  
AG Audience
English

Date, Time and Location

Friday, 1 March 2024
10:00
60 Minutes
E1 5
002
Saarbrücken

Abstract

Data is today generated on smart devices at the edge, shaping a decentralized data ecosystem comprising multiple data owners (clients) and a service provider (server). Clients interact with the server with their personal data for specific services, while the server performs analysis on the joint dataset. However, the sensitive nature of the involved data, coupled with inherent misalignment of incentives between clients and the server, breeds mutual distrust. Consequently, a key question arises: How to facilitate private data analytics within a decentralized data ecosystem, comprising multiple distrusting parties?

My research shows a way forward by designing systems that offer strong and provable privacy guarantees while preserving data functionality. I accomplish this by systematically exploring the synergy between cryptography and differential privacy, exposing their rich interconnections in both theory and practice. In this talk, I will focus on two systems, CryptE and EIFFeL, which enable privacy-preserving query analytics and machine learning, respectively.

Contact

Annika Meiser
+49 681 9303 9105
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Virtual Meeting Details

Zoom
668 9092 0474
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logged in users only

Annika Meiser, 02/28/2024 16:57
Lena Schneider, 02/26/2024 09:03
Annika Meiser, 01/19/2024 11:14
Annika Meiser, 01/18/2024 12:35 -- Created document.